# Physics Working Group

The primary physics task of STAR is to study the formation and characteristics of the quark-gluon plasma (QGP), a state of matter believed to exist at sufficiently high energy densities. Detecting and understanding the QGP allows us to understand better the universe in the moments after the Big Bang, where the symmetries (and lack of symmetries) of our surroundings were put into motion.

Unlike other physics experiments where a theoretical idea can be tested directly by a single measurement, STAR must make use of a variety of simultaneous studies in order to draw strong conclusions about the QGP. This is due both to the complexity of the system formed in the high-energy nuclear collision and the unexplored landscape of the physics we study. STAR therefore consists of several types of detectors, each specializing in detecting certain types of particles or characterizing their motion. These detectors work together in an advanced data acquisition and subsequent physics analysis that allows final statements to be made about the collision.

The physics of star can be divided into several topics, with many overlaps between topics. In STAR, each of these topics is explored within a physics working group which develops the analysis techniques and software needed to focus on its interests.

# Heavy Flavor

Depending on the energy scale, there are two mechanisms that generate quark masses with different degrees of importance: current quark masses are generated by the electroweak symmetry breaking mechanism (Higgs mass) and spontaneous chiral symmetry breaking leads to the constituent quark masses in QCD (QCD mass). The QCD interaction strongly affects the light quarks (u, d, s) while the heavy quark masses (c, b, t) are mainly determined by the Higgs mechanism. In high-energy nuclear collisions at RHIC, heavy quarks are produced through gluon fusion and qq¯ annihilation. Heavy quark production is also sensitive to the parton distribution function. Unlike the light quarks, heavy quark masses are not modified by the surrounding QCD medium (or the excitations of the QCD medium) and the value of their masses is much higher than the initial excitation of the system. It is these differences between light and heavy quarks in a medium that make heavy quarks an ideal probe to study the properties of the hot and dense medium created in high-energy nuclear collisions.

Heavy flavor analyses at STAR can be separated into quarkonia, open heavy flavor and heavy flavor leptons.

# #9995# DNP (fall meeting) 2010

Abstracts for DNP (fall meeting) 2010 (Nov. 2-6, 2010, Santa Fe, NM)

• Wenqin Xu

Title: Extracting bottom quark production cross section from p+p collisions at RHIC

The STAR collaboration has measured the non-photonic electron (NPE) production at high transverse momentum (pT ) at middle rapidity in p + p collisions at sqrt(s) = 200 GeV at the Relativistic Heavy Ion Collider (RHIC). The relative contributions  of bottom and charm hadrons to NPE have also been obtained through electron hadron azimuthal
correlation studies. Combining these two,  we are able to determine the high pT mid-rapidity electron spectra
from bottom and charm decays, separately.

PYTHIA with different tunes and FONLL calculations have been compared  with this measured electron spectrum
from bottom decays to extract the bb-bar differential cross section after normalization to the measured spectrum.
The extrapolation of the total bb-bar production cross section in the whole kinematic range and its dependence
on spectrum shapes from model calculations will also be discussed.

• Yifei Zhang

Title: Open charm hadron reconstruction via hadronic decays in p+p collisions at $sqrt{s}$ = 200 GeV

Heavy quarks are believed to be an ideal probe to study the properties of the QCD medium produced in the relativistic heavy ion collisions. Heavy quark production in elementary particle collisions is expected to be better calculated in the perturbative QCD. Precision understanding on both the charm production total cross section and the fragmentation in p+p collisions is a baseline to further explore the QCD medium via open charm and charmonium in heavy ion collisions.
Early RHIC measurements in p+p collisions which were carried out via semi-leptonic decay electrons provides limited knowledge on the heavy quark production due to the incomplete kinematics, the limited momentum coverage and the mixed contribution from various charm and bottom hadrons in the electron approach. In this talk, we will present
the reconstruction of open charm hadrons (D0 and D*) via the hadronic decays in p+p collisions at $sqrt{s}$ = 200 GeV in the STAR experiment. The analysis is based on the large p+p minimum bias sample collected in RHIC Run9. The Time-Of-Flight detector, which covered 72% of the whole barrel in Run9, was used to improve the decay daughter
identification. Physics implications from this analysis will be presented.

• Xin Li

Title: Non-photonic Electron Measurements in 200 GeV p+p collisions at RHIC-STAR

Compared to the light quarks, heavy quarks are produced early in the collisions and interact very differently with the strongly couple QGP(sQGP) created at RHIC. In addition, their large masses are created mostly from the spontaneous symmetry breaking. All these features make heavy quark an ideal probe to study the sQGP. One of the critical references in these studies is the heavy quark production in p+p collisions, which also provides a crucial test to the pQCD. Measuring electrons from heavy quark semi-leptonic decay (non-photonic electron) is one of the major approaches to study heavy quark production at RHIC.

We will present STAR measurements on the mid-rapidity non-photonic electron production at pT>2 GeV/c in 200 GeV p+p collisions using the datasets from the 2008 and 2005 runs, which have dramatically different photonic backgrounds. We will compare our measurements with the published results at RHIC and also report the status of the analysis at pT<2 GeV/c using the dataset from the 2009 run.

• Jonathan Bouchet

Title: Reconstruction of charmed decays using microvertexing techniques with the STAR Silicon Detectors

Due to their production at the early stages, heavy flavor particles are of interest to study the properties of the matter created in heavy ion collisions. Direct topological reconstruction of $D$ and $B$ mesons, as opposed to indirect methods using semi-leptonic decay channels [1], provides a precise measurement and thus disentangles the $b$ and $c$ quarks contributions [2].

In this talk we present a microvertexing technique used in the reconstruction of $D^{0}$ decay vertex ($D^{0} \rightarrow K^{-}\pi^{+}$) and its charge conjugate. The significant combinatorial background can be reduced by means of
secondary vertex reconstruction and other track cut variables. Results of this method using the silicon detector information of the STAR experiment at RHIC will be presented for the Au+Au system at $\sqrt{s_{NN}}$ = 200 GeV.

[1]A. Abelev et al., Phys. Rev. Lett. {\bf 98} (2007) 192301
[2]N. Armesto et al., Phys. Lett. B{\bf 637} (2006) 362-366.

# #9996# Hard Probe 2010

Abstracts for 2010 Hard Probe Meeting (Oct. 10-15, 2010, Eilat, Israel)

•  Wei Xie
Title: Heavy flavor production and heavy flavor induced correlations at RHIC

Heavy quarks are unique probes to study the strongly coupled Quark-Gluon Plasma created at RHIC. Unlike light quarks, heavy quark masses come mostly from spontaneous symmetry breaking, which makes them ideal for studying the medium's QCD properties. Due to their large masses, they are produced early in the collisions and are expected to interact with the medium quite differently from that of light quarks. Detailed studies on the production of open heavy flavor mesons and heavy quarkonium in heavy-ion collisions and the baseline $p+p$ and $d+A$ collisions provide crucial information in understanding the medium's properties. With the large acceptance TPC, Time of Flight, EM Calorimeter and future Heavy-Flavor Tracker, STAR has the capabilities to study heavy quark production in the dense medium in all different directions. In this talk, we will review the current status as well as the future perspectives of heavy quark studies in STAR experiment.

• Zebo Tang

Title: $J/\psi$ production at high pT at STAR

The $c\bar{c}$ bound state $J/\psi$ provides a unique tool to probe the hot dense medium produced in heavy-ion collisions, but to date its production mechanism is not understood clearly neither in heavy-ion collisions nor in hadron hadron collisions. Measurement of $J/\psi$ production at high $p_T$ is particularly interesting since at high $p_T$
the various models give different predictions. More over some model calculations on $J/\psi$ production are only applicable at intermediate/high $p_T$. Besides, high $p_T$ particles are widely used to study the parton-medium interactions in heavy-ion collisions. In this talk, we will present the measurement of mid-rapidity (|y|<1) $J/\psi \rightarrow e^+e^-$ production at high $p_T$ in p+p and Cu+Cu collisions at 200 GeV, that used a trigger on electron energy deposited in Electromagnetic Calorimeter. The $J/\psi$ $p_T$ spectra and nuclear modification factors will be compared to model calculations to understand its production mechanism and medium modifications. The $J/\psi$-hadron azimuthal angle correlation will be presented to disentangle $B$-mesons contributions to inclusive $J/\psi$. Progresses
from on-going analyses in p+p collisions at 200GeV taken in year 2009 high luminosity run will be also reported.

• Rosi Reed

Title: $\Upsilon$ production in p+p, d+Au, Au+Au collisions at $\sqrt{{S}_{NN }} =$ 200 GeV in STAR

export PYTHIA8DATA=$PYTHIA8/xmldoc  Run configure with the option for shared-library creation turned on. ./configure --enable-shared make  ## Install ROOT from source Download the source code for ROOT from http://root.cern.ch/ and compile. tar zxvf root_v5.20.00.source.tar.gz cd root/ ./configure linux --with-pythia6-libdir=$HOME/pythia6 \
--enable-pythia8 \
--with-pythia8-incdir=$PYTHIA8/include \ --with-pythia8-libdir=$PYTHIA8/lib
make
make install

Set the following environment variables (preferably in /etc/profile.d/root.sh):
export ROOTSYS=/usr/local/root
export PATH=$PATH:$ROOTSYS/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$ROOTSYS/lib:/usr/local/pythia6
export MANPATH=$MANPATH:$ROOTSYS/man

You should be good to go. Try running the following Pythia 6 and 8 sample macros:
root $ROOTSYS/tutorial/pythia/pythiaExample.C root$ROOTSYS/tutorial/pythia/pythia8.C


Pibero Djawotho
Last updated on Sun Jul 20 23:35:39 EDT 2008

# Hot Strips Identified by Hal Spinka

## Run 7137036 Sector 9

### Strips 09V064

Pibero Djawotho
Last updated Wed Jul 23 03:40:54 EDT 2008

# Strips from Weihong's 2006 ppLong 20 runs

## Energy [GeV] vs. strip id

2006ppLongRuns.pdf

## Raw ADC vs. strip id

Pibero Djawotho
Last updated Thu Jul 24 10:35:50 EDT 2008

# G/h Discrimination Algorithm (Willie)

My blog pages, from first to last:

01/25: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/jan/25/photon-analysis-progress-week-1-21-08-1-25-08.  This post discusses the problem with the spike in secondary tracks at eta=1 in our single-particle simulations.

01/28: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/jan/28/further-qa-plots.  This post has QA plots for every particle sample Ross generated, both in the barrel and in the endcap.

02/01: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/01/more-qa-plots-time-efficiencies.  This post has QA plots for gamma and piminus (barrel and endcap) as well as reconstruction efficiencies.

02/04: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/04/photon-qa-efficiency-plots-error-bars.  This post adds error bars to the reconstruction efficiencies for the photon barrel sample.

02/05: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/05/first-clustering-plots.  This post has the first clustering plots, for muons and gammas (barrel only), showing cluster energy, energy-weighted cluster eta and phi, and the number of seeds and clusters passing the thresholds for each event.

02/12: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/12/preshower-plots.  This post has preshower plots from the gamma barrel sample, but the plots are of all preshowers in the event and use the preshower information generated by the BEMC simulator and so are not useful.

02/13: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/13/more-clustering-plots.  This post has geant QA plots combined with the clustering plots from 02/05 above, but for the gamma and piminus barrel samples.

02/19: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/19/cluster-track-matching-plots.  This post investigates the cluster-to-track matching for the gamma barrel sample, using a simple distance variable d=sqrt((delta eta)^2+(delta phi)^2)) to match clusters to tracks and plotting the resulting energy distributions, the energy ratio, etc.

02/21: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/21/more-preshower-plots.  This post plots preshower distributions but uses the preshower information from the BEMC simulator and so is not useful.

02/28: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/feb/28/further-preshower-plots-not-completed-yet.  Figures 1, 3, and 5 in this post plot the geant preshower energy deposition for gammas, piminuses, and muons (Figs 2, 4, and 6 plot reconstructed preshower information again and so are not useful).

03/04: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/04/muon-preshower-plots.  This post expands on the post of 02/28, with additional plots using the geant preshower information, including preshower cluster energy vs. tower cluster energy.

03/06: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/06/first-physics-cuts.  This post basically recaps the previous post and adds a cut: unfortunately the cut is based partly on the thrown particle energy and so is not useful.

03/18: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/18/smd-qa-plots.  This post plots energy-weighted SMD phi and eta distributions, as well as the total energy deposited in the BSMDE and BSMDP strips located behind a cluster.

03/28: http://drupal.star.bnl.gov/STAR/blog-entry/wleight/2008/mar/28/smd-clustering-plots.  This post contains SMD clustering plots for barrel gamma and piminus samples.

# Neutral Pions 2005: Frank Simon

Information about the 2005 Spin analysis (focused on A_LL and <z>, some QA plots for cross section comparisons) will be archived here. The goal is obviously the 2005 Pi0 spin paper.

# Invariant Mass and Width: Data-MC

Here I show the invariant masses and corresponding widths I obtain using my cross section binning. These are compared to MC values.

The Method:

• Invariant mass Data histograms (low mass background and combinatoric background subtracted) are fitted with a gaussian in the range 0.1 - 0.18 GeV/c^2. This gives the mass (gaussian mean) and width (gaussian sigma)
• MC invariant mass histograms are obtained from correctly associated MC Pi0s after reconstruction. No weighting of the different partonic pt samples is performed. This can (and will) introduce a bias
• Then the same fitting procedure as for data is applied

The results are shown in the two figures below.

Mass:

Width:

# Neutral Pion Paper: 2005 ALL & <z>

Neutral Pion Paper for 2005 data: Final Results.

There are two spin plots planned for the paper, one with the 2005 A_LL and one with the <z>. In addition to this the cross section will be included (analysis by Oleksandr used for publication).

## Final result for A_LL:

Figure 1: Double longitudinal spin asymmetry for inclusive Pi0 production. The curves show predictions from NLO pQCD calculations based on the gluon distributions from the GRSV, GS-C and DSSV global analyses. The systematic error shown by the gray band does not include a 9.4% normalization uncertainty due to the polarization measurement.

The chi2/ndf for the different model curves are:

GRSV Std: 0.740636
GRSV Max: 3.49163
GRSV Min: 0.94873
GRSV Zero: 0.546512
GSC: 0.513751
DSSV: 0.543775

## Final Result for <z>:

Figure 2: Mean momentum fraction of Pi0s in their associated jet as a function of p_T for electromagnetically triggered events. The data points are plotted at the bin center in pion p_T and are not corrected for acceptance or trigger effects. Systematic errors, estimated from a variation of the cuts, are shown by the grey band underneath the data points. The lines are results from simulations with the PYTHIA event generator. The solid line includes detector effects simulated by GEANT, while the dotted line uses jet finding on the PYTHIA particle level. The inset shows the distribution of pT, π / pT, Jet for one of the bins, together with a comparison to PYTHIA with a full detector response simulation.

# <z> Details

<z> Details

The goal of this analysis is to relate the neutral pions to the jets they are embedded in. The analysis is done using the common spin analysis trees, which provide the necessary tools to combine the jet and neutral pion analysis.

A neutral pion is associated to a parent jet if it is within the jet cone of 0.4 in eta and phi. To avoid edge effects in the detector, only neutral pions with 0.3 < eta < 0.7 are accepted.

## Cut details:

E_neutral / E_total < 0.95

higher energy photon of Pi0 > 2.8 GeV (HT1 trigger); > 3.6 GeV (HT2 trigger)

combination HT1/HT2: below 5.7 GeV only HT1 is used, above that both HT1 and HT2 are accepted

The final result uses both HT1 and HT2 triggers, but a trigger separated study has also been done, as shown below. There, HT2 includes only those HT2 triggers that do not satisfy HT1 (because of prescale).

Figure 1: <z> for Pi0 in jets as a function of p_T for HT1 and HT2 triggers. Also shown is the mean jet p_T as a function of pion p_T.

## Bin-by-Bin momentum ratio

Figure 2: Bin-by-bin ratio of pion to jet p_T. The <z> is taken from the mean of these distributions, the error is the error on the mean. A small fraction of all entries have higher Pi0 p_T than jet p_T. Similar behavior is also observed for Pythia MC with GEANT jets. This obviously increases the <z>. An alternative would be to reject those events. The agreement with MC becomes worse if this is done.

Here is the data - MC comparison for 3 of the above bins. For the simulation, the reconstruction of the Pi0 is not required to keep statistics reasonable, so the true Pi0 pt is used. However, the MC jet finding uses all momenta after Geant, this is why the edges are "smoother" in the MC plot than in the data plots. Since <z> is an average value, this is not expected to be affected by this, since on average the Pi0 pt is reconstructed right.

Figure 3: Data / MC for Bin 5: 6.7 to 8 GeV

Figure 4: Data / MC for Bin 6: 8 to 10 GeV

Figure 5: Data / MC for Bin 7: 10 to 12 GeV

# A_LL Details

Details on the A_LL result and the systematic studies:

## The result in numbers:

 Bin [GeV] in bin A_LL stat. error syst. error 1 4.17 0.01829 0.03358 0.01603 2 5.41 -0.01913 0.02310 0.01114 3 7.06 0.00915 0.03436 0.01343 4 9.22 -0.06381 0.06366 0.01862

## A_LL as separated by trigger:

Figure 1: A_LL as a function of p_T for HT1 (black) and HT2 (red) triggers separately. HT1 here is taken as all triggers that satisfy the HT1 requirement, but not HT2. Since the HT2 prescale is one, there are very little statistics for HT1 at the highest p_T. The highest p_T point for HT1 is outside the range of the plot, and has a large error bar. The high p_T HT1 data is used in the combined result.

## Systematics: Summary

 Bin 1 Bin 2 Bin 3 Bin4 relative luminosity 0.0009 0.0009 0.0009 0.0009 non-longitudinal pol. 0.0003 0.0003 0.0003 0.0003 beam background 0.0012 0.0084 0.0040 0.0093 yield extraction 0.0144 0.0044 0.0102 0.0116 invariant mass background 0.0077 0.0061 0.0080 0.0108 total 0.01603 0.01114 0.01343 0.01862

The first two systematics are common to all spin analyses. The numbers here are taken from the jet analysis. No Pi0 non-longitudinal analysis has been performed due to lacking statistics. These systematics are irrelevant compared to the others.

The analysis specific systematics are determined from the data, and as such are limited by statistics. The real systematic limit of a Pi0 analysis with a very large data sit will be much lower.

For the yield extraction systematic the invariant mass cuts for the pion yield extraction are varied. The systematic is derived from the maximum change in asymmetry with changing cuts.

For the beam background, the systematic is derived by studying how much A_LL changes when the beam background is removed. This is a conservative estimate that covers the scenario that only half of the background is actually removed. The asymmetry of the background events is consistent with zero.

For the invariant mass background systematic, A_LL is extracted in three invariant mass bins outside the signal region. The amount of background under the invariant mass peak (includes combinatorics, low mass and others) is estimated from the invariant mass distribution as shown below. For all three bins, the background A_LL is consistent with zero, a "worst case" of value + 1 sigma is assumed as deviation from the signal A_LL.

## Invariant mass distribution:

Figure 2: Invariant mass distribution for HT1 events, second p_T bin. The red lines are the MC expectations for Pi0 and Eta, the green line is low mass background, the magenta line is combinatoric background, the thick blue line is a pol2 expectation for the other background, the blue thinner line is the total enveloppe of all contributions, compared to the data. At low mass, the background is overestimated.

## Other systematic studies: False Asymmetries

False asymmetries (parity-violating single spin asymmetries) were studied to exclude systematic problems with spin asignments and the like. Of course the absence of problems in the jet analysis with the same data set makes any issues very unlikely, since jet statistics allow much better verifications than Pi0s. Still, single spin asymmetries were studied, and no significant asymmetries were observed. For both triggers, both asymmetries (yellow and blue) and for all p_T bins the asymmetries are consistent with zero, in most cases within one sigma of zero. So there are no indications for systematic effects. The single spin asymmetries are shown below:

Figure 3: Single spin asymmetry epsilon_L for the blue beam.

Figure 4: Single spin asymmetry epsilon_L for the yellow beam.

# Neutral strange particle transverse asymmetries (tpb)

## Neutral strange particle transverse asymmetry analysis

Here is information regarding my analysis of transverse asymmetries in neutral strange particles using 2006 p + p TPC data. This follows-on from and expands upon the earlier analysis I did, which can still be found at star.bnl.gov/protected/strange/tpb/analysis/. Comments, questions, things-you'd-like-to-see-done and so forth are welcomed. I'll catalogue updates in my blog as I make them.

The links listed below are in 'analysis-order'; best to use these for navigation rather than the alphabetically listed links Drupal links below/in the sidebar.

e-mail me at tpb@np.ph.bham.ac.uk

# Data used

## Data used in analysis

Data used for this analysis is 2006 p+p 200 GeV data taken with transverse polarisation, trigger setup "ppProductionTrans". This spanned days 97 (7th April) to 129 (9th May) inclusive. Trigger bemc-jp0-etot-mb-l2jet (ID 127622) is used. A file catalogue query with the following conditions gives a list of runs for which data is available:

trgsetupname=ppProductionTrans,tpc=1,year=2006,sanity=1,collision=pp200,magscale=FullField,filename~physics,library=SL06e,production=P06ie

This generates a list of 549 runs. These runs are then compared against the spin PWG run QC (see http://www.star.bnl.gov/protected/spin/sowinski/runQC_2006) and are rejected if any of the following conditions are true:

• The run is marked as unusable
• The run has a jet patch trigger problem
• The run has a spin bits problem
• The run is unchecked

This excludes 172 runs, leaving 377 runs to be analysed.

I use a Maker class to create TTrees of event objects with V0 and spin information for these runs. Code for the Maker and Event classes can be found at /star/u/tpb/StRoot/StTSAEventMaker/ and /star/u/tpb/StRoot/StV0NanoDst/ respectively. Events are accepted only if they fulfill the following criteria:

• Event contains specified trigger ID
• StSpinDbMaker::offsetBX48minusBX7() returns zero

TTrees are produced for 358 runs (19 produce no/empty output), yielding 2,743,396 events.

The vertex distribution of events from each run are then checked by spin bits. A Kolmogorov test (using ROOT TH1::KolmogorovTest) is used to compare the vertex distributions for (4-bit) spin bits values 5, 6, 9 and 10. If any of the distributions are inconsistent, the run is rejected. Each run's mean event vertex z position is then plotted. Figure 1 shows the distribution, fitted with a Gaussian. A 3σ cut is applied and outlier runs rejected. 38 runs are rejected by these further cuts. The remaining 320 runs, spanning 33 RHIC fills and comprising 2,500,421 events, are used in the analysis.

 Figure 1: Mean event vertex z for each run. The red lines indicate the 3σ cut.

# Double spin asymmetry

## Double spin asymmetry

I measure a double spin asymmetry defined as follows

 Equation 1

where N-(anti)parallel indicates yields measured in one half of the detector when the beam polarisations are aligned (opposite) and P1 and P2 are the polarisations of the beams. Accounting for the relative luminosity, these yields are given by

 Equation 2 Equation 2

where the arrows again indicate beam polarisations. Figures one and two show the fill-by-fill measurement of ATT, corrected by the beam polarisation, summed over all pT.

 Figure 1: K0S ATT fill-by-fill Figure 2: Λ ATT fill-by-fill

# Energy loss identification

## Energy loss particle identification

The Bethe-Bloch equation can be used to predict charged particle energy loss. Hans Bichsel's model adds to this and the Bichsel function predictions for particle energy loss are compared with measured values. Tracks with dE/dx sufficiently far from the predicted value are rejected. e.g. when selecting for Λ hyperons, the positive track is required to have dE/dx consistent with that of a proton, and the negative track consistent with that of a π-minus.

The quantity σ = sqrt(N) x log( measured dE/dx - model dE/dx ) / R is used to quantify the deviation of the measured dE/dx from the model value. N is the number of track hits used in dE/dx determination and R is a resolution factor. A cut of |σ| < 3 applied to both V0 daughter tracks was found to significantly reduce the background with no loss of signal. Figures one to three below show the invariant mass distriubtions of the V0 candidates accepted and rejected and table one summarises the results of the cut. Background rejection is more successful for (anti-)Λ than for K0S because most background tracks are pions; the selection of an (anti-)proton daughter rejects the majority of the background tracks.

 Figure 1a: Invariant mass spectrum of V0 candidates under K0s hypothesis passing dE/dx cut Figure 1b: Invariant mass spectrum of V0 candidates under K0s hypothesis failing dE/dx cut Figure 2a: Invariant mass spectrum of V0 candidates under Λ hypothesis passing dE/dx cut Figure 2b: Invariant mass spectrum of V0 candidates under Λ hypothesis failing dE/dx cut Figure 3a: Invariant mass spectrum of V0 candidates under anti-Λ hypothesis passing dE/dx cut Figure 3b: Invariant mass spectrum of V0 candidates under anti-Λ hypothesis failing dE/dx cut

Species Pass (millions) Fail (millions) % pass
K0S 95.5 48.9 66.2 %
Λ 32.5 111.9 22.5 %
anti-Λ 11.8 132.5 8.2 %

Table 1

# Geometrical cuts

## Geometrical cuts

Energy loss cuts are successful in eliminating a significant portion of the background, but further reduction is required to give a clear signal. In addition final yields are calculated by a bin counting method, which requires that the background around the signal peak has a straight line shape. Therefore additional cuts are placed on the V0 candidates based on the geometrical properties of the decay. There are five quantities on which I chose to cut:

• Distance of closest approach (DCA) of the V0 candidate to the primary vertex: if the V0 candidate is a genuine particle, its momentum vector should track back to the interaction point. Spurious candidates will not necessarily do so, therefore an upper limit is placed on the approach distance of the V0 to the interaction point.
• DCA between the daughter tracks: due to detector resolution the daughter tracks never precisely meet, but placing an upper limit of the minimum distance of approach reduces background from spurious track crossings.
• DCAs of the positive and negative daughter tracks to the primary vertex: the daughter tracks are curved due to the magnetic field and a neutral strange particle will decay some distance from the interaction point. Therefore the daughter tracks should not extrapolate back to the primary vertex, but to some distance away from it. Placing a lower limit on this distance can reduce background from tracks originating from the interaction point.
• V0 decay distance: neutral strange particles decay weakly, with cτ ~ cm, so the decay vertex should typically be displaced from the interaction point. A lower limit placed on the decay distance of the V0 helps eliminate backgrounds from particles originating at the interaction point.

I wrote a class to help perform tuning of these geometrical cut quantities (see /star/u/tpb/StRoot/StV0CutTuning/) by a "brute force" approach; different permutations of the above quantities were attempted, and the resulting mass spectra analysed to see which permutations gave the best balance of background reduction and signal retention. In addition, the consistency of the background to a straight-line shape was required. Due to the limits on statistics, signal retention was considered a greater priority than background reduction. The cut values I decided upon are summarised in table one. Figures one to three show the resulting mass spectra (data are from all runs). Yields are calculated from the integral of bins in the signal (red) region minus the integrals of bins in the background (green) regions. Poisson (√N) errors are used. The background regions are fitted with a straight line, skipping the intervening bins. The signal to background quoted is the ratio of the maximum bin content to the value of the background fit evaluated at that mass. Note that the spectra have the the dE/dx cut included in addition to the geometrical cuts.

Species Max DCA V0 to PV* Max DCA between daughters Min DCA + daughter to PV Min DCA − daughter to PV Min V0 decay distance
K0S 1.0 1.2** 0.5 0.0** 2.0**
Λ 1.5 1.0 0.0** 0.0** 3.0
anti-Λ 2.0** 1.0 0.0** 0.0** 3.0

Table 1: Summary of geometical cuts. All cut values are in centimetres.

* primary vertex
** default cut present in micro-DST

 Figure 1: Final K0S mass spectrum with all cuts applied. Figure 2: Final Λ mass spectrum with all cuts applied. Figure 3: Final anti-Λ mass spectrum with all cuts applied.

# Single spin asymmetry using cross formula

## Single Spin asymmetry using cross formula

Equation one shows the cross-formula used to calculate the single spin asymmetry.

 Equation 1

where N is a particle yield, L(eft) and R(ight) indicate the side of the polarised beam to which the particle is produced and arrows indicate the polarisation direction of the beam. Equation one cancels acceptance and beam luminosity and allows simply the raw yields to be used for the calculation. The asymmetry can be calculated twice; once for each beam, summing over the polarisation states of the other beam to leave it "unpolarised". I previously used only particles produced at forward η when calculating the blue beam asymmetry, and backward η for yellow, but I now sum over the full η range for each. Equations two and three give the numbers for up/down polarisation for blue (westward at STAR) and yellow (eastward) beams respectively in terms of the contributions from the four different beam polarisation permutations, and these permutations are related to spin bits numbers in table one.

 Equation 2 Equation 3

(in e.g. N(upUp), The first arrow refers to yellow beam polarisation, the second to blue beam.)

Beam polarisation 4-bit spin bits
Yellow Blue
Up Up 5
Down Up 6
Up Down 9
Down Down 10
Table 1

The raw asymmetry is calculated for each RHIC fill, then divided by the polarisation for that fill to give the physics asymmetry. Final polarisation numbers (released December 2007) are used. The error on the raw asymmetry is calculated by propagation of the √(N) errors calculated for each particle yield. The final asymmetry error incorporates the polarisation error (statistical and systematic errors summed in quadrature). The fill-by-fill asymmetries for each K0S and Λ for each beam are shown in figures one and two. Anti-Λ results shall be forthcoming. An average asymmetry is calculated by performing a straight line χ2 fit through the fill-by-fill values with ROOT. Table one summarises the asymmetry results. The asymmetry error is the error from the ROOT fit and is statistical only. All fits give a good χ2 per degree of freedom and are consistent with zero within errors.

 Figure 1a: K0S blue beam asymmetry Figure 1b: K0S yellow beam asymmetry Figure 2a: Λ blue beam asymmetry Figure 2b: Λ yellow beam asymmetry

The above are summed over the entire pT range available. I also divide the data into different transverse momentum bins and calculate the asymmetry as a function of pT. Figures three and four show the pT-dependent asymmetries. No pT dependence is discernible.

 Figure 3a: K0S pT-dependent blue beam AN Figure 3b: K0S pT-dependent yellow beam AN Figure 4a: Λ pT-dependent blue beam AN Figure 4b: Λ pT-dependent yellow beam AN

# Single spin asymmetry utilising relative luminosity

## Single spin asymmetry making use of relative luminosity

I also calculate the asymmetry via an alternative method, making use of Tai Sakuma's relative luminosity work. The left-right asymmetry is defined as

 Equation 1

where NL is the particle yield to the left of the polarised beam. The decomposition of the up/down yields into contributions from the four different beam polarisation permutations is the same as given in the cross-asymmetry section (equations 2 and 3). Here, the yields must be scaled by the appropriate relative luminosity, giving the following relations:

 Equation 2 Equation 3

The relative luminosities R4, R5 and R6 are the ratios of luminosity for, respectively, up-up, up-down and down-up bunches to that for down-down bunches. I record the particle yields for each polarisation permutation (i.e. spin bits) on a run-by-run basis, scale each by the appropriate relative luminosity for that run, then combine yields from all the runs in a given fill to give fill-by-fill yields. These are then used to calculate a fill-by-fill raw asymmetry, which is scaled by the beam polarisation. The figures below show the resultant fill-by-fill asymmetry for each beam and particle species, summed over all pT. The fits are again satisfactory, and give asymmetries consistent with zero within errors, as expected.

 Figure 1a: Blue beam asymmetry for K0S Figure 1b: Yellow beam asymmetry for K0S Figure 2a: Blue beam asymmetry for Λ Figure 2b: Yellow beam asymmetry for Λ

# V0 decays

## V0 decays

The appearance of the decay of an unobserved neutral strange particle into two observed charged daughter particles gives rise to the terminology 'V0' to describe the decay topology. The following neutral strange species have been analysed:

Species Decay channel Branching ratio
K0S π+ + π- 0.692
Λ p + π- 0.639
anti-Λ anti-p + π+ 0.639

Candidate V0s are formed by combining together all possible pairs of opposite charge-sign tracks in an event. The invariant mass of the V0 candidate under different decay hypotheses can then be determined from the track momenta and the daughter masses (e.g. for Λ the positive daughter is assumed to be a proton, the negative daughter a π-minus). Raw invariant mass spectra are shown below. The spectra contain three contributions: real particles of the species of interest; neutral strange particles of a different species; combinatorial background from chance positive/negative track crossings.

 Figure 1: Invariant mass spectrum under K0s hypothesis
 Figure 2: Invariant mass spectrum under Λ hypothesis
 Figure 3: Invariant mass spectrum under anti-Λ hypothesis

Selection cuts are applied to the candidates to suppress the background whilst maintaining as much signal as possible. There are two methods for reducing background; energy-loss particle identification and geometrical cuts on the V0 candidates.

# 2008.01.30 Selecting gamma-jet candidates out of the jet trees

Ilya Selyuzhenkov January 30, 2008

### Data set

jet trees by Murad Sarsour for pp2006 run, runId=7136022 (~60K events, no triggerId cuts yet)

### Jets gross features

• Figure 1: Distribution of number of jets per event. Same data on a log scale is here.

• Figure 2: Distribution of electromagnetic energy (EM) fraction, R_EM, for di-jet events (number of jets/event = 2).
R_EM = [E_t(endcap)+E_t(barrel)]/E_t(total).
Black histogram is for R_EM1 = max(Ra, Rb), red is for R_EM2 = min(Ra, Rb).
Ra and Rb are EM fraction for jets in the di-jet event.
Same data on a log scale is here.

### Gamma-jet isolation cuts list:

1. selecting di-jet events with one of the jet dominated by EM energy,
and another one with more hadronic energy:

R_EM1 >0.9 and R_EM2 < 0.9

2. selecting di-jet events with jets pointing opposite in azimuth:

cos(phi1 - phi2) < -0.9

3. requiring the number of associated charged tracks with a first jet (with maximum EM fraction) to be less than 2:

nChargeTracks1 < 2

4. requiring the number of fired EEMC towers associated with a first jet (with maximum EM fraction) to be 1 or 2:

0 < nEEMCtowers1 < 3

### Applying gamma-jet isolation cuts

• Figure 3: Distribution of eta vs number of EEMC towers for the first jet (with maximum EM fraction).
Cuts:1-3 applied (no 0 < nEEMCtowers1 < 3 cut).

• Figure 4: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 applied

• Figure 5: Distribution of mean transverse momentum, < pt1 >, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 applied

• Figure 6: Distribution of pseudorapidity, eta1, of the first jet (with maximum EM fraction)
vs pseudorapidity, eta2, of the second jet.
Cuts:1-4 applied

• Figure 7: Distribution of azimuthal angle, phi1, of the first jet (with maximum EM fraction)
vs azimuthal angle, phi2, of the second jet.
Cuts:1-4 applied

• Figure 8: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse energy sum for the EEMC towers associated with this jet.
Cuts:1-4 applied

• Figure 9: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 + Et(EEMC) > 3.0 GeV

# 2008.02.13 Gamma-jet candidates: EEMC response

Ilya Selyuzhenkov February 13, 2008

### Data sample

Gamma-jet selection cuts are discussed here. There are 278 candidates found for runId=7136022.
Transverse momentum distribution for the gamma-jet candidates can be found here.

### Vertex z distribution for di-jet and gamma-jet events

• Figure 1: Vertex z distribution.

Red line presents gamma-jet candidates (scaled by x50). Black is for all di-jet events.
Same data on a log scale is here.

• Figure 2: Average vertex z as a function of transverse momentum of the fist jet (with a largest EM energy fraction).
Red is for gamma-jet candidates. Black is for all di-jet events.
Strong deviation from zero for gamma-jet candidates at pt < 5GeV?

### EEMC response for the gamma-jet candidate

EEMC response event by event for all 278 gamma-jet candidate can be found in this pdf file.
Each page shows SMD/Tower energy distribution for a given event:

1. First row on each page shows SMD response
for the sector which has a maximum energy deposited in the EEMC Tower
(u-plane is on the left, v-plane is on the right).

2. In the left plot (u-plane energy distribution) numerical values for
pt of the first jet (with maximum EM fraction) and the second jet are given.

3. In addition, fit results assuming gamma (single Gaussian, red line) or
neutral pion (double Gaussian, blue line ~ red+green) hypotheses are given.

4. m_{gamma gamma} value (it is shown in the right plot for v-plane).

If m_{gamma gamma} value is negative, then the reconstruction procedure has failed
(for example, no uv-strips intersection found, or tower energy and uv-strips intersection point mismatch, etc).
EEMC response for these "bad" events can be found in this pdf file.

If reconstruction procedure succeded, then
m_{gamma gamma} gives reconstructed invariant mass assuming that two gammas hit the calorimeter.

Figure 3: invariant mass distribution (assuming pi0 hypothesis).

Note, that I'm still working on my fitting algorithm (which is not explained here),
and fit results and the invariant mass distribution will be updated.

5. It is also shown the ratio for each u/v plane
of the integrated single Gaussian fit (red line) to the total energy in the plane
(look for "gamma U/V " values on the right v-plane plot).

6. Second and third rows on each page show the energy deposition in the
tower, pre-shower1, pre-shower2, and post-shower as a function of eta:phi (etaBin:phiBin).

7. Last row shows the hit distribution in the SMD for all sectors
(u-plane on the left, v-plane of the right).

### Playing with a different cuts

Trying to isolate the real gammas which hits the calorimeter,
I have sorted events into different subsets based on the following set of cuts:

1. EEMC gamma-jet cuts (energetic photon hits EEMC with pt similar or greater to that of the opposite jet)

if (invMass < 0) reject
if (jet2_pt > jet1_pt) reject
if (jet1_pt < 7) reject
if (minFraction < 0.75) reject
(minFraction = gamma U/V - is a fraction of the integrated single Gaussian peak to the total energy in the uv-plane)

Figure 4: Sample gamma-jet candidate EEMC response
(all gamma-jet candidates selected according to these conditions can be found in this pdf file):

2. EEMC pi0 cuts:

if (invMass < 0) reject
if (jet2_pt < jet1_pt) reject
if (jet2_pt < 7) reject
if (minFraction < 0.75) reject

Event by event EEMC response for pi0 (di-jet) candidates
selected according to these conditions can be found in this pdf file.

# 2008.02.20 Gamma-jet candidates: more statistics from jet-trees

Ilya Selyuzhenkov February 20, 2008

### Short summary

After processing all available jet-trees for pp2006 (ppProductionLong),
and applying all "gamma-jet" cuts (which are described below):

• there are 47K di-jet events selected

• for pt1>7GeV there are 5,4K gamma-jet candidates (3,7K with an additional cut of pt1>pt2)

• Figure 1: 2,4K events with both pt1, pt2 > 7GeV

• 721 candidates within a range of pt1>pt2 and both pt1, pt2 > 7 GeV

### Data set

jet trees by Murad Sarsour for pp2006 run, number of runs processed: 323
4.7M di-jet events found (no triggerId cuts yet)

### Di-jets gross features

• Figure 2: Distribution of electromagnetic energy (EM) fraction, R_EM, for di-jet events (number of jets/event = 2).
R_EM = [E_t(endcap)+E_t(barrel)]/E_t(total).
Black histogram is for R_EM1 = max(Ra, Rb), red is for R_EM2 = min(Ra, Rb).
Ra and Rb are EM fraction for jets in the di-jet event.
Same data on a log scale is here.

### Gamma-jet isolation cuts:

1. selecting di-jet events with one of the jet dominated by EM energy,
and another one with more hadronic energy:

R_EM1 >0.9 and R_EM2 < 0.9

2. selecting di-jet events with jets pointing opposite in azimuth:

cos(phi1 - phi2) < -0.9

3. requiring the number of associated charged tracks with a first jet (with maximum EM fraction) to be less than 2:

nChargeTracks1 < 2

4. requiring the number of fired EEMC towers associated with a first jet (with maximum EM fraction) to be 1 or 2:

0 < nEEMCtowers1 < 3

### Applying gamma-jet isolation cuts

• Figure 3: Distribution of eta vs number of EEMC towers for the first jet (with maximum EM fraction).
Cuts:1-3 applied (no 0 < nEEMCtowers1 < 3 cut).

• Figure 4: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 applied

• Figure 5: Distribution of mean transverse momentum, < pt1 >, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 applied

• Figure 6: Distribution of pseudorapidity, eta1, of the first jet (with maximum EM fraction)
vs pseudorapidity, eta2, of the second jet.
Cuts:1-4 applied

• Figure 7: Distribution of azimuthal angle, phi1, of the first jet (with maximum EM fraction)
vs azimuthal angle, phi2, of the second jet.
Cuts:1-4 applied

• Figure 8: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse energy sum for the EEMC towers associated with this jet.
Cuts:1-4 applied

• Figure 9: Distribution of transverse momentum, pt1, of the first jet (with maximum EM fraction)
vs transverse momentum, pt2, of the second jet.
Cuts:1-4 + Et(EEMC) > 3.0 GeV

# 2008.02.27 Tower based clustering algorithm, and EEMC/BEMC candidates

Ilya Selyuzhenkov February 27, 2008

### Gamma-jet candidates before applying clustering algorithm

Gamma-jet isolation cuts:

1. selecting di-jet events with the first jet dominated by EM energy,
and the second one with a large fraction of hadronic energy:

R_EM1 >0.9 and R_EM2 < 0.9

2. selecting di-jet events with jets pointing opposite in azimuth:

cos(phi1 - phi2) < -0.8

3. requiring no charge tracks associated with a first jet (jet with a maximum EM fraction):

nCharge1 = 0

Figure 1: Transverse momentum

Figure 2: Pseudorapidity

Figure 3: Azimuthal angle

### Tower based clustering algorithm

• for each gamma-jet candidate finding a tower with a maximum energy
associated with a jet1 (jet with a maximum EM fraction).

• Calculating energy of the cluster by finding all adjacent towers and adding their energy together.

• Implementing a cut based on cluster energy fraction, R_cluster, where

R_cluster is defined as a ratio of the cluster energy
to the total energy in the calorimeter associated with a jet1.
Note, that with a cut Ncharge1 =0, energy in the calorimeter is equal to the jet energy.

### Distribution of cluster energy vs number of towers fired in EEMC/BEMC

Figure 4: R_cluster vs number of towers fired in EEMC (left) and BEMC (right). No pt cuts.

Figure 5: R_cluster vs number of towers fired in EEMC (left) and BEMC (right). Additional cut: pt1>7GeV

Figure 6: jet1 pseudorapidity vs number of towers fired in EEMC (left) and BEMC (right).

### R_cluster>0.9 cut: EEMC vs BEMC gamma-jet candidates

EEMC candidates: nTowerFiredBEMC=0
BEMC candidates: nTowerFiredEEMC=0

Figure 7: Pseudorapidity (left EEMC, right BEMC candidates)

Figure 8: Azimuthal angle (left EEMC, right BEMC candidates)

Figure 9: Transverse momentum (left EEMC, right BEMC candidates)

### Number of gamma-jet candidates with an addition pt cuts

Figure 10: Transverse momentum (left EEMC, right BEMC candidates): pt1>7GeV

Figure 11: Transverse momentum (left EEMC, right BEMC candidates): pt1>7 and pt2>7

# 2008.03.03 EEMC SMD: u/v-strip energy distribution

Ilya Selyuzhenkov March 03, 2008

Data set: ppLongitudinal, runId = 7136033.

Some observations/questions:

1. In general distributions look clean and good

2. Sectors 7 and 9 for v-plane and sector 7 for u-plane are noise.

3. Sector 9 has a hot strip (id ~ 120)

4. In sector 3, strips id=0-5 in v-plane are hot (see figure 2 right, bottom)

5. Sectors 2 and 8 in u-plane and sectors 3 and 9 in v-plane have missing strips id=283-288?

6. Strips 288 are always empty?

Figure 1:Average energy E in the strip vs sector and strip number (max < E > = 0.0027)
same figure on a log scale

Figure 2: Average energy E for E>0.02 (max < E > = 0.0682)
Same figure on a log scale

# 2008.03.12 Gamma-jet candidates: 2-gammas invariant mass and Eemc response

Ilya Selyuzhenkov March 12, 2008

### Gamma-jet candidates: 2-gammas invariant

Note: Di-jet transverse momentum distribution for these candidates can be found on figure 11 at this page

Figure 1:Invariant mass distribution for gamma-jet candidates assuming pi0 (2-gammas) hypothesys

Figure 2:Invariant mass distribution for gamma-jet candidates assuming pi0 (2-gammas) hypothesys
with an additional SMD isolation cut: gammaFraction >0.75
GammaFraction is defined as ratio of the integral
other SMD strips for the first peak to the total energy in the sector

### EEMC response for the gamma-jet candidates (gammaFraction >0.75)

1. pdf file (first 100 events) with event by event EEMC response for the candidates reconstructed into pion mass (gammaFraction >0.75)

2. pdf file with event by event EEMC response for the candidates not reconstructed into pion mass
(second peak not found), but has a first peak with gammaFraction >0.75.

# 2008.03.20 Sided residual and chi2 distribution for gamma-jet candidates

Ilya Selyuzhenkov March 20, 2008

### Side residual (no pt cut on gamma jet-candidates)

The procedure to discriminate gamma candidate from pions (and other background)
based on the SMD response is described at Pibero's web page.

Figure 1: Fit integral vs maximum residual for gamma-jet candidates requesting
no energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).

Black line is defined from MC simulations (see Jason's simulation web page, or Pibero's page above).

Figure 2: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
no energy deposited in pre-shower 1 cluster and
no energy deposited in post-shower cluster (this cut is not really essential in demonstrating the main idea)

Figure 3: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

### Side residual: first and second jet pt are greater than 7GeV

Event by event EEMC response for gamma-jet candidates for the case of
no energy deposited in the EEMC pre-shower 1 and 2 can be found in this pdf file

Figure 4: Fit integral vs maximum residual for gamma-jet candidates requesting
no energy deposited in the EEMC pre-shower 1 and 2

Figure 5: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
no energy deposited in pre-shower 1 cluster and
no energy deposited in post-shower cluster

Figure 6: Fit integral vs maximum residual for gamma-jet candidates requesting requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

### Monte Carlo shape

Event Monte Carlo shape allows to distinguish gammas from background by cutting at chi2/ndf < 0.5
(although the distribution looks wider than for the case of Will's shape).

Figure 7: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
no energy deposited in both clusters of pre-shower 1 and 2

Figure 8: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

### Will''s shape

Less clear where to cut on chi2?

Figure 9: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
no energy deposited in both clusters of pre-shower 1 and 2

Figure 10: Chi2/ndf for gamma-jet candidates using Monte Carlo shape requesting
non-zero energy deposited in both clusters of pre-shower 1 and 2

# 2008.03.26 Sided residual and chi2 distribution for gamma-jet candidates (pre1,2 sorted)

Ilya Selyuzhenkov March 26, 2008

### gamma-jet candidates (no pt cut)

Definitions:

• F_peak - integral for a fit within [-2,2] strips around SMD u/v peak
• D_peak - integral over the data within [-2,2] strips around SMD u/v peak
• D_tail^max (D_tail^min) - maximum (minimum) integral over the data tail within +-[3,30] strips from a SMD u/v peak
• F_tail is the integral over the fit tail within [3,30] strips from a SMD u/v peak.
• Maximum residual = D_tail^max - F_tail

All results are for combined distributions from u and v planes: ([u]+[v])/2
Gamma-jet isolation cuts described here

1. Matching between 3x3 tower cluster and u-v high strip intersection
2. At least 4 strips fired within [-2,2] strips from a peak

Figure 1: F_peak vs maximum residual
for various cuts on energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).

Figure 2: F_data vs D_tail^max
Note:This plot is fit independend (only the peak position is defined based on the fit)

Figure 3: F_data vs D_tail^max-D_tail^max

Figure 4: Gamma transverse momentum vs jet transverse momentum

### gamma-jet candidates: pt > 7GeV

Figure 5: F_peak vs maximum residual
for various cuts on energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).

Figure 6: F_data vs D_tail^max
Note:This plot is fit independend (only the peak position is defined based on the fit)

Figure 7: F_data vs D_tail^max-D_tail^max

Figure 8: Gamma transverse momentum vs jet transverse momentum

### gamma-jet candidates: eta, phi, and max[u,v] strip distributions (no pt cuts)

Figure 9: Gamma pseudorapidity vs jet pseudorapidity

Figure 10: Gamma azimuthal angle vs jet azimuthal angle
Note: for the case of Pre1>1 && Pre2==0 there is an enhancement around phi_gamma = 0?

Figure 11: maximum strip in v-plane vs maximum strip in u-plane

### Chi2 distribution for gamma-jet candidates (no pt cuts)

Figure 12:Chi2/ndf for gamma-jet candidates using Monte Carlo shape (combined for [u+v]/2 plane )

Figure 13:Chi2/ndf for gamma-jet candidates (combined for [u+v]/2 plane ) using Will's shape

# 2008.03.28 EEMC SMD shapes: gamma's from gamma-jets (data), MC, and eta-meson analysis

Ilya Selyuzhenkov March 28, 2008

### Some observations:

1. SMD data-driven shapes from different analysis are in a good agreement (Figure 1, upper left plot)
2. Overall MC shape is too narrow compared to the data shapes (Figure 1, upper left plot)
3. Shapes are similar with or without gamma-jet 7GeV pt cut (compare Figures 1 and 2),
what may indicate that shape is independent on energy (at least within our kinematic limits).
4. Data-driven and MC shapes are getting close to each other (Figure 4, upper left plot)
when requiring no energy above threshold in both preshower layers and
with suppressed contribution from pi0 background.
The latter is achieved by using the information on
reconstructed invariant mass of 2gamma candidates (compare Figure 3 and 4).

One interpretation of this can be that in Monte Carlo simulations
the contribution from the material in front of the detector is underestimated

5. Energy distribution for each strip in the SMD peak does not looks like a Gaussian (Figure 5),
what makes very difficult to interpret results obtained from chi2 analysis (Figure 6-8).
6. Triple Gaussian fit gives a better description of the data shapes,
compared to the double Gaussian function (compare red and black lines on Figure 1-4)

Figure 1: EEMC SMD shape comparison for various preshower cuts
(black points shows u-plane shape only, v-plane results can be found here)

Figure 2: EEMC SMD shape comparison for various preshower cuts with gamma-jet pt cut of 7GeV
(black points shows u-plane shape only, v-plane results can be found here)

Figure 3: Shapes with an additional cut on 2-gamma candidates within pi0 invariant mass range.
Sample invariant mass distribution using "simple" pi0 finder can be found here
(black points shows u-plane shape only, v-plane results can be found here)

Figure 4: Shapes for the candidates when "simple" pi0 finder failed to find a second peak
(black points shows u-plane shape only, v-plane results can be found here)

Figure 5: Strip by strip SMD energy distribution.
Only 12 strips from the right side of the maximum are shown.
Zero strip (first upper left plot) corresponds to the high strip in the shape
Note, that already at the 3rd strip from a peak,
RMS values are comparable to those for a mean, and for a higher strips numbers RMS starts to be bigger that mean.
(results for u-plane only, v-plane results can be found here)

### Comparing chi2 distributions for gamma-jet candidates using MC, Will, and Pibero's shapes

Results for side residual (together with pt, eta, phi distributions) for gamma-jet candidates can be found at this web page

Red histograms on Figures 6-8 shows chi2 distribution from MC-photons (normalized at chi2=1.4)
Blue histograms on Figures 6-8 shows chi2 distribution from MC-pions (normalized at chi2=1.4)

Figure 6: Chi2/ndf for gamma-jet candidates using Monte Carlo shape

Figure 7: Chi2/ndf for gamma-jet candidates using Will's shape (derived from eta candidates based on Weihong's pi0-finder)

Figure 8: Chi2/ndf for gamma-jet candidates using Pibero's shape (derived from eta candidates)

# 2008.04.02 EEMC SMD shapes: data-driven (eta, gamma-jet) vs Monte Carlo (single gamma, gamma-jet)

Ilya Selyuzhenkov April 02, 2008

### Some observations:

1. SMD data-driven shapes from eta-meson and gamma-jet studies
are in a good agreement for different preshower conditions
(compage Fig.1 green circles/triangles in upper-left/bottom-right plots)
2. single gamma MC shapes show preshower dependance,
but they are still narrower compared to the data shapes
(compare Fig.1 green circles vs blue open squares)
3. MC shapes for gamma-jet and single gamma are consistent (Fig.1, bottom right plot)

Figure 1: EEMC SMD shape comparison for various preshower cuts
Note:Only MC gamma-jet shape (open red squares) is the same on all plots

# 2008.04.02 Sided residual: Using data driven gamma-jet shape (3 gaussian fit)

Ilya Selyuzhenkov April 02, 2008

Figure 1: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
No EEMC SMD based cuts

Figure 2: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
"Simple" pi0 finder can not find a second peak

Figure 3: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
"Simple" pi0 finder reconstruct the invarian mass within [0.1,0.18] range

Figure 4: Side residual distribution (Projection for side residual in Figs.1-3 on vertical axis)

Figure 5: Signal (green: m < 0) vs background (black, red) separation

# 2008.04.02 Sided residual: single gamma Monte-Carlo simulations

Ilya Selyuzhenkov April 02, 2008

### Side residual: single gamma Monte-Carlo simulations

Figure 1: Side residual for various cuts on energy deposited in the EEMC pre-shower 1 and 2
No EEMC SMD based cuts

# 2008.04.03 chi2-shape subtraction for different Preshower conditions

Ilya Selyuzhenkov April 03, 2008

#### Request from Hal Spinka:

Hi Ilya,

I think you gave up on the chi-squared method too quickly, and am sorry I missed the phone meeting last week. So, I would like to make a request that will hopefully take a minimal amount of your time to show that all is okay. Then, if there is a delay in getting the sided residual information out and into the beam use request, you can still fall back on the chi-squared method.

In your March 28 posting, Figure 8 at the bottom, I would like to get numerical values for the events per bin for the black curves. I won't use the preshower1>0 and preshower2=0 data, so those you don't need to send. Also, I won't use the red or blue curve information.

I think your problem has been that you normalized your curves at chi-squared/ndf = 1.4 instead of the peak. What I plan to do is to normalize the (pre1=0, pre2=0) to the (pre1=0, pre2>0) data in the peak and subtract. The (pre1=0, pre2=0) set should have some single photons, but also some multiple photons. The (pre1=0, pre2>0) should also have single photons, and more multiple photons, since the chance that one of them will convert is larger. The difference should look roughly like your blue curve, though perhaps not exactly if Pibero's mean shower shape is not perfect (which it isn't). I will do the same thing with taking the difference between (pre1>0, pre2>0) and (pre1=0, pre2=0), and again the difference should look roughly like your blue curve. The (pre1>0, pre2>0) data should have even larger fraction of multiple photons than either of the other two data sets. I would expect the two difference curves to look approximately the same.

Hope this is possible for you to do. Since our reduced chi-squared curve looks so much like the one from CDF, I am pretty confident that we are okay, but this should be checked to convince people that we are not doing anything terribly wrong.

Dear Hal,

I have tried to implement your idea and produce a figure attached.

There are 4 plots in it:

1. Upper left plot shows normalized to unity (at maximum) chi2 distribution (obtained with Pibero shape for gamma-jet candidates) for a different pre1, pre2 conditions

2. Upper right plot shows bin-by-bin difference: a) between normalized chi2 for pre1=0, pre2>0 and pre1=0, pre2=0 (red) and b) between normalized chi2 for pre1>0, pre2>0 and pre1=0, pre2=0 (blue)

3. Bottom left Same as upper right, but normalization were done based on the integral within [-4,4] bins around maximum.

4. Bottom right Same as for upper right, but with a different normalization ([-4,4] bins around maximum)

I have also tried to normalized by the total integral, but the results looks similar.

Figure 1: See description above

Figure 2: Same without log scale (See description above)

# 2008.04.09 Applying gamma-jet reconstruction algorithm for gamma-jet simulated events

Ilya Selyuzhenkov April 09, 2008

Data sample:
Monte-Carlo gamma-jet sample for partonic pt range of 5-7, 7-9, 9-11,11-15, 15-25, 25-35 GeV.

Analysis: Simulated MuDst files were first processed through jet finder algorithm (thanks to Renee Fatemi),
and later analyzed by applying gamma-jet isolation cuts (see this link for details) and studying EEMC SMD response (see below).
To test the algorithm, Geant records were not used in this analysis.
Further studies based on Geant records (yield estimates, etc) are ongoing.

#### EESMD shapes comparison

Figure 1:Comparison between shower shape profile for data and MC.
Black circles shows results for MC gamma-jet sample (all partonic pt).
For v-plane results see this figure

#### Correlation between gamma and jet pt, eta, phi

Figure 2:Gamma vs jet transverse momentum.

Figure 3:Gamma vs jet azimuthal angle.

Figure 4:Gamma vs jet pseudo-rapidity.

#### Results from maximum sided residua study

Definitions for F_peak, D_peak, D_tail^max (D_tail^min) can be found here

Figure 5:F_peak vs maximum residual
for various cuts on energy deposited in the EEMC pre-shower 1 and 2
(within a 3x3 clusters around tower with a maximum energy).
Shower shape used to fit data is fixed to the shape from the previous gamma-jet study of real events

#### Postshower to SMD[uv] energy ratio

Figure 8:Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Figure 8a:
Same as figure 8, but for gamma-jet candidates from the real data (no pt cuts).
Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Figure 8b:
Comparison between gamma-jet candidates from data with different preshower conditions.
Points are normalized in peak to the case of pre1 > 0, pre2 > 0

Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Figure 8c:
Comparison between gamma-jet candidates from Monte-Carlo simulations with different preshower conditions.
Points are normalized in peak to the case of pre1 > 0, pre2 > 0

Logarithmic fraction of energy in post shower (3x3 cluster) to the total energy in SMD u- and v-planes

Figure 9: Jet neutral energy fraction
Figure 10: High v-strip vs u-strip
Figure 11: energy post shower (3x3 cluster)
Figure 12: Peak energy SMD-u
Figure 13: Peak energy SMD-v
Figure 14: Gamma phi
Figure 15: Gamma pt
Figure 16: Gamma eta
Figure 17: Delta gamma-jet pt
Figure 18: Delta gamma-jet eta
Figure 19: Delta gamma-jet phi

#### chi2 distributions

Figure 20:chi2 distribution using "standard" MC shape

Figure 21:chi2 distribution using Pibero shape

# 2008.04.16 Sided residual: Data Driven MC vs raw MC vs 2006 data

Ilya Selyuzhenkov April 16, 2008

Figure 1: Sided residual for raw MC (partonic pt 9-11)

Figure 2: Sided residual for data-driven MC (partonic pt 9-11)

Figure 3: Sided residual for data (pp Longitudinal 2006)

#### Different analysis cuts vs number of events which passed the cut

1. N_events : total number of di-jet events found by the jet-finder for gamma in eta region [1,2]
(Geant record is used to get this number)
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy.
4. R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet
5. N_ch=0 : no charge tracks associated with a gamma candidate
6. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
7. N_(5-strip clusler)^u > 3 : minimum number of strips in EEMC SMD u-plane cluster around peak
8. N_(5-strip cluster)^v > 3 : minimum number of strips in EEMC SMD v-plane cluster around peak
9. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
10. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluser

Figure 4: Number of events which passed various cuts (MC data, partonic pt 9-11)

# 2008.04.17 Sided residual: Data Driven MC vs raw MC (partonic pt=5-35) vs 2006 data

Ilya Selyuzhenkov April 17, 2008

MC data for different pt weigted according to Michael Betancourt web page:
weight = xSection[ptBin] / xSection[max] / nFiles

Figure 1: Sided residual for raw MC (partonic pt 5-35)
(same plot for partonic pt 9-11)

Figure 2: Sided residual for data-driven MC (partonic pt 5-35)
(same plot for partonic pt 9-11)

Figure 3: Sided residual for data (pp Longitudinal 2006)

Figure 4: Sided residual for data (pp Longitudinal 2006)

Figure 5: Sided residual for data (pp Longitudinal 2006)

Figure 6: pt(gamma) from geant record vs
pt(gamma) from energy in 3x3 tower cluster and position for uv-intersection wrt vertex
(same on a linear scale)

Figure 7: pt(gamma) from geant record vs
pt(jet) as found by the jet-finder

Figure 8: gamma pt distribution:
data-driven MC (red) vs gamma-jet candidates from pp2006 longitudinal run (black).
MC distribution normalized to data at maximum for each preshower condition

#### Different analysis cuts vs number of events which passed the cut

1. N_events : total number of di-jet events found by the jet-finder for gamma in eta region [1,2]
(Geant record is used to get this number)
2. cos(phi_gamma - phi_jet) < -0.8 : gamma-jet opposite in phi
3. R_{3x3cluster} > 0.9 : Energy in 3x3 cluster of EEMC tower to the total jet energy.
4. R_EM^jet < 0.9 : neutral energy fraction cut for on away side jet
5. N_ch=0 : no charge tracks associated with a gamma candidate
6. N_bTow = 0 : no barrel towers associated with a gamma candidate (gamma in the endcap)
7. N_(5-strip clusler)^u > 3 : minimum number of strips in EEMC SMD u-plane cluster around peak
8. N_(5-strip cluster)^v > 3 : minimum number of strips in EEMC SMD v-plane cluster around peak
9. gamma-algo fail : my algorithm failed to match tower with SMD uv-intersection, etc...
10. Tow:SMD match : SMD uv-intersection has a tower which is not in a 3x3 cluser

Figure 9: Number of events which passed various cuts (MC data, partonic pt 5-35)
Red: cuts applied independent
Black: cuts applied sequential from left to right

# 2008.04.23 Gamma-jet candidates: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet)

Ilya Selyuzhenkov April 23, 2008

### Sided residual: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet)

MC data for different partonic pt are weigted according to Michael Betancourt web page:
weight = xSection[ptBin] / xSection[max] / nFiles

Figure 1:Sided residual for data-driven gamma-jet MC events (partonic pt 5-35)

Figure 2:Sided residual for data-driven jet-jet MC events (partonic pt 3-55)

Figure 3:Sided residual for data (pp Longitudinal 2006)

Figure 4:pt(gamma) vs pt(jet) for data-driven gamma-jet MC events (partonic pt 5-35)

Figure 5:pt(gamma) vs pt(jet) for data-driven jet-jet MC events (partonic pt 3-55)

Figure 6:pt(gamma) vs pt(jet) for data (pp Longitudinal 2006)

# 2008.05.05 pt-distributions, sided residual (data vs dd-MC g-jet and bg di-jet)

Ilya Selyuzhenkov May 05, 2008

Data samples:

• pp2006(long) - 2006 pp production longitudinal data after applying gamma-jet aisolation cuts
(jet-tree sample: 4.114pb^-1 from Jamie script, 3.164 pb^1 analyses).
• gamma-jet - Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV
• bg jets - Pythia di-jet sample (~4M events). Partonic pt range 3-65 GeV

Figure 1:pt distribution. MC data are scaled to the same luminosity as data
(Normalization factor: Luminosity * sigma / N_events).

Figure 2:Integrated gamma yield vs pt.
For each pt bin yield is defined as the integral from this pt up to the maximum available pt.
MC data are scaled to the same luminosity as data.

Figure 3:Signal to background ratio (all results divided by the data)

### Sided residual: pp2006 data vs data-driven MC (gamma-jet and bg:jet-jet)

You can find sided residual 2-D plots here

Figure 4:Maximum sided residual for pt_gamma>7GeV; pt_jet>7GeV

Figure 5:Fitted peak for pt_gamma>7GeV; pt_jet>7GeV

Figure 6:Max data tail for pt_gamma>7GeV; pt_jet>7GeV

Figure 7:Max minus min data tails for pt_gamma>7GeV; pt_jet>7GeV

Figure 8:Shower shapes pt_gamma>7GeV; pt_jet>7GeV

# 2008.05.08 y:x EEMC position for gamma-jet candidates

Ilya Selyuzhenkov May 08, 2008

### y:x EEMC position for gamma-jet candidates

Figure 1:y:x EEMC position for gamma-jet candidates:
Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.

Figure 2:y:x EEMC position for gamma-jet candidates:
Pythia QCD bg sample (~4M events). Partonic pt range 3-65 GeV.

Figure 3:y:x EEMC position for gamma-jet candidates:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]

Figure 3b:y:x EEMC position for gamma-jet candidates:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

### high u vs. v strip for gamma-jet candidates

Figure 4:High v-strip vs high u-strip.
Pythia gamma-jet sample (~170K events). Partonic pt range 5-35 GeV.

Figure 5:High v-strip vs high u-strip:
Pythia QCD bg sample (~4M events). Partonic pt range 3-65 GeV.

Figure 6:High v-strip vs high u-strip:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]

Figure 6b:High v-strip vs high u-strip:
pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

# 2008.05.09 Gamma-jet candidates pt-distributions and TPC tracking

Ilya Selyuzhenkov May 09, 2008

### Detector eta cut study (1< eta < 1.4):

• For a three data samples (pp2006 [long], MC gamma-jet, and MC QCD background events)
the EEMC detector eta cut of 1< eta < 1.4 has been applied.

• Although a poor statistics available for MC background QCD sample,
the signal to background ratio (red to green line ratio)
getting closer to 1:3 (expected signal to background ratio from Les study).

Figure 1:Gamma pt distribution. MC data are scaled to the same luminosity as data
(Normalization factor: Luminosity * sigma / N_events).

Figure 2:Gamma yield vs pt. MC data are scaled to the same luminosity as data.

Figure 3:Signal to background ratio (MC results are normalized to the data)

# 2008.05.14 Gamma-cluster to jet energy ratio and away side jet pt matching

Ilya Selyuzhenkov May 14, 2008

### Gamma-cluster to jet1 energy ratio

• Correlation between gamma-candidate 3x3 cluster energy ratio (R_cluster) and
number of EEMC towers in a jet1 can be found here (Fig. 4).

• Gamma pt distribution, yield and signal to background ratio plots
for a cut of R_cluster >0.9 can be found here (Figs. 1-3).

• Gamma pt distribution, yield and signal to background ratio plots
for a cut of R_cluster >0.99 are shown below in Figs. 1-3.
One can see that by going from R_cluster>0.9 to R_cluster>0.99
improves signal to background ratio from ~ 1:10 to ~ 1:5 for gamma pt>10 GeV

Figure 1:Gamma pt distribution for R_cluster >0.99.
MC results scaled to the same luminosity as data
(Normalization factor: Luminosity * sigma / N_events).

Figure 2:Integrated gamma yield vs pt for R_cluster >0.99
For each pt bin yield is defined as the integral from this pt up to the maximum available pt.
MC results scaled to the same luminosity as data.

Figure 3:Signal to background ratio for R_cluster >0.99 (all results divided by the data)
Compare this figure with that for R_cluster>0.9 (Fig. 3 at this link)

### Gamma and the away side jet pt matching

Figure 4: pt asymmetry between gamma and the away side jet (R_cluster >0.9)
for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background).
pt cut of 7 GeV for both gamma and jet has been applied.

Figure 5: signal to background ratio (R_cluster >0.9)
as a function of pt asymmetry between gamma and the away side jet
pt cut of 7 GeV for both gamma and jet has been applied.

Figure 6: pt asymmetry between gamma and the away side jet (R_cluster >0.99)
for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background).
pt cut of 7 GeV for both gamma and jet has been applied.

Figure 7: signal to background ratio
as a functio of pt asymmetry between gamma and the away side jet (R_cluster >0.99)
pt cut of 7 GeV for both gamma and jet has been applied.

Figure 8: pt asymmetry between gamma and the away side jet (R_cluster >0.99)
for a three data samples (pp2006[long] data, gamma-jet MC, QCD jets background).
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

Figure 9: signal to background ratio
as a function of pt asymmetry between gamma and the away side jet (R_cluster >0.99)
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

# 2008.05.15 Vertex z distribution for pp2006 data, MC gamma-jet and QCD jets events

Ilya Selyuzhenkov May 15, 2008

Figure 1:Vertex z distribution for pp2006 (long) data [eemc-http-mb-l2gamma:137641 trigger]
Note: In the upper right plot (pre1=0, pre2>0) one can see
a hole in the acceptance in the range bweeeen z_vertex -10 to 30 cm (probably due to SVT construction)

Figure 1b:Vertex z distribution for pp2006 (same as Fig. 1, but on a linear scale)

Figure 2:Vertex z distribution for three different data samples
MC results scaled to the same luminosity as data

Figure 3:Vertex z distribution for three different data samples
pt cut of 7 GeV for gamma and 5GeV for the away side jet has been applied.

# 2008.05.20 Shower shapes sorted by pre-shower, z-vertex and gamma's eta, phi, pt

Ilya Selyuzhenkov May 20, 2008

### Gamma-jet algorithm and isolation cuts:

1. Selecting only di-jet events identified by the STAR jet finder algorithm,
with jets pointing opposite in azimuth:
cos(phi_jet1 - phi_jet2) < -0.8

2. Select jet1 with a maximum neutral energy fraction (R_EM1).
This is our gamma candidate, for which we further require:
• No charge tracks associated with jet1 (default jet radius is 0.7):
nChargeTracks_jet1 = 0
Note, that this charge track veto only works
in the EEMC region where we do have TPC tracking
• No barrel towers associated with jet1 (pure EEMC jet):
nBarrelTowers_jet1 = 0
• Ratio of the energy in the 3x3 EEMC high tower cluster
to the total je